Multigenic copy number alterations

多基因拷贝数改变

基本信息

批准号：
10115639
负责人：
Scott Powers
金额：
$ 55.32万
依托单位：
STATE UNIVERSITY NEW YORK STONY BROOK
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-03-15 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10115639
关键词：
11q13 14q13 Address Affect Attention Automobile Driving Bar Codes Benchmarking Biological Breast Cancer cell line Breast Epithelial Cells CCND1 gene CRISPR/Cas technology Cataloging Cell model Cells Clinical effectiveness Communities Computational algorithm Computing Methodologies Control Locus Cre-LoxP DHFR gene DNA DNA copy number Data Set Dependence ERBB2 gene Endometrial Carcinoma Engineering Epidermal Growth Factor Receptor Event Evolution Experimental Models FGF19 gene GRB7 gene Gene Combinations Gene Mutation Genes Genetic Genetic Recombination Genetic study Genome engineering Genomics Glioblastoma Goals Human Individual KRAS2 gene Link Malignant - descriptor Malignant Neoplasms Malignant neoplasm of esophagus Malignant neoplasm of liver Malignant neoplasm of lung Malignant neoplasm of ovary Methods Methotrexate Modeling Mutation Nature Oncogenes Oncogenic Oncoproteins PDGFRA gene PIK3CA gene Pathway interactions Patients Phenotype Phosphoproteins Phosphorylation Plasmids Play Primary carcinoma of the liver cells Property Proteins Proteomics RNA Research Resources Role Sampling Site Squamous Cell Lung Carcinoma Stretching Structure System Technology Testing The Cancer Genome Atlas Therapeutic Time Work anticancer research base cancer genome cancer therapy combinatorial comparative computerized tools design improved malignant breast neoplasm malignant stomach neoplasm molecular subtypes mutant new therapeutic target next generation sequencing novel novel therapeutics predictive test screening success tool tumor tumor progression

项目摘要

Project Summary DNA copy number alterations (CNAs) are oncogenic drivers for many types of human cancer. For some cancers, e.g. certain ovarian, breast and endometrial cancers, it is very likely that CNAs, comprise the bulk of genetic alterations responsible for their highly malignant properties. CNAs may also be responsible for driving squamous carcinoma of the lung and for subsets of gastric and esophageal cancers. Relatively little attention is being paid to understanding this class of genetic alterations. More importantly, from a cancer treatment perspective, there is no roadmap for determining whether they induce selective dependencies that could be utilized for developing new therapeutics. As our group and others have discovered in the past several years, the vast majority of CNAs contain multiple driver genes, and this makes it considerably more difficult to study how they impact cancer progression compared to single-gene events. The overall goal of this project is to develop new tools and models to investigate multigenic CNAs so that they can be more readily studied and utilized in developing new therapeutics. In Aim 1, we will combine CRISPR/Cas9 and Cre-Lox genome engineering to accurately model multigenic CNAs and determine how they impact oncogenic phenotypes in normal mammary epithelial cells, similar to how mutations in single-gene alterations such as PIK3CA are currently studied. Once we have validated these new cell models, we will screen for induced dependencies. In Aim 2, we will develop and implement computational methods to extract information about specific CNAs from the warehouse of information present in large-scale integrated cancer genome datasets. We have extensive preliminary results that validate this approach, including the prediction of CNA-selective dependencies. Lastly, to truly understand how multigenic CNAs play a role in cancer, we must functionally probe the interactions between multiple drivers. We previously demonstrated that these interactions were key features of the oncogenicity of the 14q13 amplicon in lung cancer and 11q13 amplicon in liver cancer. Thus, our final goal is to develop and implement generalizable methods to study genetic interactions between multiple drivers (Aim 3). Our proposal is based on the premise that CNAs are important drivers in cancer but that the current research approach needs to be improved. The clinical effectiveness of targeted treatments for patients with HER2-amplified breast cancers underscores the enormous translational potential of CNAs. By developing the tools and models for CNAs described in this proposal, we will make a significant impact on understanding multigenic CNAs and will lay the groundwork for identifying associated dependencies and therapeutic strategies.

项目摘要 DNA拷贝数改变（CNA）是许多类型的人类癌症的致癌驱动因素。对于某些人癌症，例如某些卵巢，乳房和子宫内膜癌，CNA很可能包括大部分导致其高度恶性特性的遗传改变。 CNA也可能负责驾驶肺癌的鳞状癌和胃癌和食管癌的亚群。相对较少的关注正在为理解这类遗传改变而付费。更重要的是，通过癌症治疗观点，没有路线图可以确定它们是否引起选择性依赖性用于开发新的治疗剂。正如我们的小组和其他人在过去几年中发现的那样，绝大多数CNA都包含多个驱动器基因，这使得研究它们如何影响癌症变得更加困难与单基因事件相比，进展。该项目的总体目标是开发新工具和研究多基因CNA的模型，以便可以更容易地研究和利用它们疗法。在AIM 1中，我们将结合CRISPR/CAS9和CRE-LOX基因组工程以准确建模多基因CNA并确定它们如何影响正常乳腺上皮细胞中的致癌表型，类似于目前研究了单基因改变（例如PIK3CA）中的突变。一旦我们有经过验证的这些新细胞模型，我们将筛选诱导的依赖关系。在AIM 2中，我们将发展和实施计算方法以从仓库中提取有关特定CNA的信息大规模综合癌症基因组数据集中存在的信息。我们有广泛的初步结果这可以验证这种方法，包括预测CNA选择性依赖性。最后，真正了解多基因CNA如何在癌症中发挥作用，我们必须在功能上探测多个的相互作用司机。我们先前证明了这些相互作用是14q13的致癌性的关键特征肺癌的扩增子和肝癌的第11季度扩增子。因此，我们的最终目标是开发和实施研究多个驱动因素之间遗传相互作用的可推广方法（AIM 3）。我们的建议基于CNA是癌症中重要驱动因素的前提，但目前需要改进研究方法。针对性治疗对患者的临床治疗 HER2扩增的乳腺癌强调了CNA的巨大翻译潜力。通过开发该提案中描述的CNA的工具和模型，我们将对理解产生重大影响多基因CNA，将为识别相关依赖性和治疗性奠定基础策略。